On the Uniqueness of the Canonical Polyadic Decomposition of third-order tensors --- Part I: Basic Results and Uniqueness of One Factor Matrix
arXiv:1301.4602 · doi:10.1137/120877234
Abstract
Canonical Polyadic Decomposition (CPD) of a higher-order tensor is decomposition in a minimal number of rank-1 tensors. We give an overview of existing results concerning uniqueness. We present new, relaxed, conditions that guarantee uniqueness of one factor matrix. These conditions involve Khatri-Rao products of compound matrices. We make links with existing results involving ranks and k-ranks of factor matrices. We give a shorter proof, based on properties of second compound matrices, of existing results concerning overall CPD uniqueness in the case where one factor matrix has full column rank. We develop basic material involving -th compound matrices that will be instrumental in Part II for establishing overall CPD uniqueness in cases where none of the factor matrices has full column rank.
28 pages
References in corpus (1)
Cited by in corpus (14)
- Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
- On the Uniqueness of the Canonical Polyadic Decomposition of third-order tensors --- Part II: Uniqueness of the overall decomposition
- Canonical polyadic decomposition of third-order tensors: reduction to generalized eigenvalue decomposition
- An algorithm for generic and low-rank specific identifiability of complex tensors
- Generic uniqueness conditions for the canonical polyadic decomposition and INDSCAL
- DOA Estimation for Transmit Beamspace MIMO Radar via Tensor Decomposition with Vandermonde Factor Matrix
- Overview of Constrained PARAFAC Models
- Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation
- Estimating multivariate latent-structure models
- Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting
- Tensor rank and entanglement of pure quantum states
- Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
- A generalization of Kruskal's theorem on tensor decomposition
- Minimality and uniqueness for decompositions of specific ternary forms